Method and apparatus for controlling at least one quality feature of a running material web

ABSTRACT

A method and apparatus for controlling at least one quality feature of a running material web, in particular a fibrous web, during its production, including a measurement system, an electronic control and/or an evaluation unit. The measurement system measures the quality feature on a running material web repeatedly over the entire width of the material web. The electronic control and/or evaluation unit determines the variability of the quality feature through a variance component analysis, which is conducted on the basis of the profile measure values of a measured value set including several consecutive CD profiles in the machine direction, the set having been recorded during a selectable previous time window. The result of this variance component analysis is also drawn on to control the quality feature.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates to a method and an apparatus for controlling atleast one quality feature of a running material web, in particular afibrous web, during its production. The fibrous web can be in particulara paper web or a paperboard web.

2. Description of the Related Art

Quality controls on a paper web are based on two-dimensionally recordedquality data (profile measured values). The data are recorded by way ofa quality measurement system (QMS) using scanners, associated withtraversing measurement systems or by simultaneous recording of qualitydata at numerous measurement points in the transverse direction of theweb.

The data received are first processed into a suitable form forcontrolling features of the web in the machine direction (MD) or in thecross direction (CD).

In the case of the control methods and apparatuses known to date, the MDcontrol is used to control the mean quality over the web width throughintervention in the settings in the region of the paper machine,including the calender or coater, or in the region of the wet end, suchas in the region of the pulp feed to the paper machine. Hence for MDcontrol, the current mean paper quality is calculated at each controlmoment from the previous measured values, namely through formation ofthe mean value of all measured values in the direction transverse to theweb. This calculation of the current mean paper quality from theprevious measured values can be further improved somewhat through anadditional use of filterings, extrapolations, estimate functions or thelike. The data thus created for the MD control are referred to as the MDprofile.

The CD control is used to control the deviations of paper quality in theCD direction of the paper web. The data thus created for the CD controlare referred to as the CD profile. In the prior art the variability ofthe paper quality can be determined from the MD or CD profile onlyincompletely or slowly. Through the calculation of the MD and CDprofiles using the formation of mean values, the original data quantityis reduced so severely as to lose information for statisticalstatements.

For example, if a traversing cross profile measurement includes 200profile data values for the web width and if it is also assumed, forexample, that the duration of a profile measurement amounts to 20seconds and MD profile values are determined computationally throughinterpolation techniques every 5 seconds from the measured data of thetraversing measurement system, then this means that only 20 MD profilevalues are determined from five traversings of the measurementinstrument in 100 seconds. This number of MD profile values is notsufficient for meaningful statistical statements.

What is needed in the papermaking art is a method for computing moreexacting statistics in a shorter period of time.

SUMMARY OF THE INVENTION

The present invention creates an improved method and an improvedapparatus with which the previously mentioned problems are eliminated.In particular, a more informative and faster assessment of thevariability of the web quality results.

A faster and more exact determination of the variability enables controlsystems to be optimally adjusted in a shorter time when a statisticalcharacteristic of the profile measured values changes. According to oneembodiment of the present invention this object is accomplished by amethod for controlling at least one quality feature of a runningmaterial web, in particular a fibrous web, during its production, inwhich, by use of a measurement system the quality feature in question ismeasured on a running material web, repeatedly over the entire width, inorder to receive profile measured values in the longitudinal direction(MD) and transverse direction (CD) of the web. This is accomplished byway of an electronic control and/or evaluation unit. The variability ofthe quality feature is determined through a variance component analysis,which is conducted on the basis of the profile measured values of ameasured value set including several consecutive CD profiles in themachine direction. The measured value set having been recorded during aselectable previous time window, and the result of this variancecomponent analysis is used to control the quality feature.

The individual measured data received from the material web in aprevious time window are simultaneously assessed and information lossesare prevented accordingly. Additionally, the system assesses thevariability of the material web far more informatively. For example, theMD variability in a respective previous time window is now assessed onthe basis of a very high number of individual measured values instead ofon the basis of only a few values as hitherto. If the statisticalproperties of the material web change, then the correspondingly fasterand more precise knowledge of the variability of the web quality permitsa faster change of the corresponding parameters of the control systemsand filterings. Faults of the production process due to overly severecontrol interventions are reduced to a minimum. Such faults can arisewhen, because of random or non-controllable faults, the control respondsseverely nevertheless and thus carries out unnecessary processinterventions. Such faults are practically ruled out according to thepresent invention.

A preferred practical embodiment of the present invention is a methodwere the variability of the quality feature is divided by way of thevariance component analysis of profile measured values into a CDvariability component in the cross direction (CD), an MD variabilitycomponent in the machine direction (MD) and a residual variabilitycomponent. Further, confidence values for the CD profile and/or the MDprofile are calculated from determined variance values, and thevariability components or confidence intervals are also drawn on tocontrol the quality feature.

It is also an advantage, in particular for the individual profilemeasured values of a respective measured value set, which is to besubjected to a variance component analysis, to be arranged by way of thecontrol and/or analysis unit in a matrix in whose lines or columns thereare CD profile measured values respectively and in whose columns orlines there are MD profile measured values respectively.

It is advantageous for the profile measured values contained in arespective measured value set, which is to be subjected to variancecomponent analysis, to be variously weighted in respect of theircontribution to the variability of the quality feature to be determined,at least partially, according to how far back they lie in time comparedto the actual moment of determining the variability. In this case,profile measured values lying relatively far back are preferablyweighted to a lesser degree.

Expediently the variance component analysis is repeated when aselectable number of new profile measured values is measured by way ofthe measurement system. The result of a respective variance componentanalysis can be used advantageously, for example, in order to change thebehavior of at least one control system provided to control the qualityfeature. It is also an advantage, in particular, for at least onecontrol parameter and/or at least one filter parameter of at least onecontrol system, provided to control the quality feature, to be variablyadjustable according to the result of a respective variance analysis.

The variance component analysis is based on at least one of thefollowing algorithms: TAPPI TIP 1101, TAPPI T585, Exact VarianceAnalysis DAHLIN. The Exact Variance Analysis is described below in moredetail. These algorithms are individually described in the followingpublications:

-   -   Dahlin, E. B., “Computational Methods in a Dedicated Computer        System for Measurement and Control on Paper Machines”, Tappi        Journal, 53, No. 6: 1100-1105, 1970;    -   Tappi T585 om-93, “Cross-machine grammage profile measurement        (gravimetric method)”, 1993;    -   Tappi TIP1101-01, “Calculation and partitioning of variance        using paper machine scanning sensor measurements”, issued 1996,        corrected 1997.

In the case of some algorithms for the variance component analysis, theresidual variability components are interpreted and referred to asshort-term deviations. Otherwise the algorithm in question cancorrespond, at least essentially, to one of the previously mentionedalgorithms.

Also used in the present invention are recursive forms of the variancecomponent analysis. In the case of a recursive algorithm, newly recordedmeasured data are linked to the last event of the variance componentanalysis such that an updated variance component analysis is obtained asthe result.

The CD variability component and the MD variability component can eachbe divided, through wavelength analysis, into a controllable fractionand a non-controllable fraction respectively.

A relatively large residual variability component, compared to thecurrent CD and MD variability components or controllable CD and MDfractions, is used by way of the control and/or analysis unit. At leastone control system is provided to control the quality feature relativelymore slowly and/or relatively more weakly on the production process.

It is also advantageous, in particular for a relatively large residualvariability component, compared to the current CD and MD variabilitycomponents or controllable CD and MD fractions, to be taken into accountsuch that the measured data are filtered relatively more intensivelyprior to calculation of the respective set-point variable for at leastone control system that is provided to control the quality features.

Another embodiment of the present inventive method is characterized inthat confidence intervals for the CD profile and/or the MD profile arecalculated by way of the control and/or analysis device from determinedvariance values. In the light of these confidence intervals, adistinction is drawn between a respective profile deviation based on areally existing cause and a deviation based at least essentially only ona residual variability component.

At least one control system provided to control the quality feature isactivated, preferably by way of the control and/or analysis device, toperform an intervention in the production process, if it wasestablished, in the light of at least one confidence interval, that thedeviation in question is based on a really existing cause.

Advantageously, at least one control system provided to control thequality feature is then actuated by way of the control and/or analysisdevice to intervene relatively more weakly, or not at all, in theproduction process, if it was established, in the light of at least oneconfidence interval, that the deviation in question is based at leastessentially only on a residual variability component.

EXAMPLES

-   -   K_(w): effective control gain    -   K₀: control parameter    -   VarRES: residual variance    -   VarCD: CD variance    -   VarCD_(R): controllable fraction of the CD variance

According to the invention it is thus possible to use the followingequations in order to determine an effective control gain:

$\begin{matrix}\left. a \right) & \; & {K_{w} = {K_{0} \cdot \frac{1}{\text{Var}{Res}}}} \\\left. b \right) & \; & {K_{w} = {K_{0} \cdot \frac{1}{\sqrt{\text{Var}{Res}}}}} \\\left. c \right) & \; & {K_{w} = {K_{0} \cdot \frac{\text{Var}{CD}_{R}}{\text{Var}{RES}}}} \\\left. d \right) & \; & {K_{w} = {K_{0} \cdot \sqrt{\frac{\text{Var}{CD}}{\text{Var}{RES}}}}}\end{matrix}$

Variations can also be used through the addition of constants,multiplication with other parameters, functions of greater complexityand/or allowance for limits etc.

It is also an advantage, in particular for the calculated variabilitycomponents and/or confidence intervals, to be drawn on by way of thecontrol and/or analysis device in assessing the quality of a processmodel, which serves to control the quality and is saved in the controland/or analysis device as software.

Any deviation of the default values of the process model from therecorded profile measured values is determined by way of the controland/or analysis device. Any determined deviation on a relatively highvariability of the quality features and/or a relatively low confidenceis weighted relatively more lowly and/or is filtered relatively moreintensively prior to further use.

The calculated variability components and/or confidence intervals canalso be taken into account when there is a change of the process modelwhich serves the quality control and is saved in the control and/oranalysis device as software. In this case account is also given to thecalculated variability components and/or confidence intervals,preferably in the form of adaptation parameters, which change thebehavior of the model.

It is also an advantage for the process model to be corrected by way ofthe control and analysis device, more slowly relative to the processmeasured via the profile measured values in the event of a relativelyhigher variability of the quality feature and/or a relatively lowerconfidence.

The quality features of the material web, in particular a paper web or apaperboard web, to be controlled can be, for example, the gsm substance,the moisture content and/or the web thickness, etc.

The object stated above is also accomplished according to the presentinvention by an apparatus for controlling at least one quality featureof a running material web, in particular a fibrous web, during itsproduction, having a measurement system for the repeated measurement ofthe respective quality feature on a running material web over the entirewidth of the material web, and an electronic control and/or evaluationunit for determining the variability of the quality feature through avariance component analysis. The analysis is carried out on the basis ofthe individual profile measured values of a measured value set includingseveral consecutive CD profiles in the machine direction, the set havingbeen recorded during a selectable previous time window, whereby theelectronic control and/or analysis unit is designed such that thecontrol of the quality feature intervenes in the process more or lessintensively according to the result of the variance component analysis.

The electronic control and/or analysis unit is preferably designed suchthat the variability of the quality feature by way of the variancecomponent analysis is divided into a CD variability component in thecross direction (CD), an MD variability component in the machinedirection (MD) and a residual variability component and/or confidencevalues for the CD profile and/or the MD profile. The confidence valuesare calculated from determined variance values, as such the control ofthe quality feature takes place according to the variability componentsor confidence intervals.

BRIEF DESCRIPTION OF THE DRAWINGS

The above-mentioned and other features and advantages of this invention,and the manner of attaining them, will become more apparent and theinvention will be better understood by reference to the followingdescription of an embodiment of the invention taken in conjunction withthe accompanying drawing, wherein:

FIG. 1 is a schematical representation of an embodiment of a method ofthe present invention for controlling quality features of a runningmaterial web.

Corresponding reference characters indicate corresponding partsthroughout the several views. The exemplification set out hereinillustrates one embodiment of the invention, in one form, and suchexemplification is not to be construed as limiting the scope of theinvention in any manner.

DETAILED DESCRIPTION OF THE INVENTION

The present invention will be described in more detail in the followingtext using an exemplary embodiment and with reference to the drawing.

The single FIGURE of the drawing shows a simplified schematicrepresentation of an apparatus for controlling at least one qualityfeature of a running material web, in particular a fibrous web, duringits production. This apparatus is assigned to a plant having a wet endand a paper machine (or coater, or calender) for producing a paper webor paperboard web. Hence in this case the apparatus is used, forexample, to control at least one quality feature of a paper web orpaperboard web.

The control apparatus includes a measurement system 10 for the repeatmeasurement of the respective quality feature on a running material webor paper web over the entire width of the material web. As is evidentfrom FIG. 1, measurement system 10 is typically arranged at the end of apaper machine 12 (or coater, or calender).

In addition the control apparatus includes an electronic control and/orevaluation unit 14 for determining the variability of the qualityfeature through a variance component analysis on the basis of therecorded profile measured values, of a measured value set includingseveral consecutive CD profiles in the machine direction. The set havingbeen recorded during a selectable previous time window.

The electronic control and/or analysis unit 14 is designed such that thecontrol of the quality feature takes place according to the result ofthe variance component analysis.

The variability of the quality feature can be divided by way of thevariance component analysis into a CD variability component in the crossdirection (CD), an MD variability component in the machine direction(MD) and a residual variability component. Alternatively, or inaddition, it is possible for confidence intervals for the CD profileand/or the MD profile to be calculated from determined variance values.The calculated variability components or confidence intervals are drawnon to control the quality feature.

The individual CD and MD profile measured values of a respectivemeasured value set, which is to be subjected to a variance componentanalysis, can be arranged by way of the control and/or analysis unit ina matrix in whose lines or columns there are CD profile measured valuesrespectively and in whose columns or lines there are MD profile measuredvalues respectively. The profile measured values, contained in arespective measured value set, which is to be subjected to variancecomponent analysis, can be variously weighted in respect of theircontribution to the variability of the quality feature to be determined,at least partially according to how far back they lie in time ascompared to the actual moment of determining the variability. In thiscase, profile measured values lying relatively far back can be weightedin particular relatively more lowly.

The variance component analysis can be repeated when a selectable numberof new profile measured values are measured by way of measurement system10. The result of a respective variance component analysis can be drawnon in order to change accordingly the behavior of at least one controlsystem provided to control the quality feature. For example, at leastone control parameter and/or at least one filter parameter of at leastone control system, provided to control the quality feature, can bevariably adjustable according to the result of a respective variancecomponent analysis.

The variance component analysis can be based, for example, on at leastone of the following algorithms: TAPPI TIP 1101, TAPPI T585, DAHLINExact Variance Analysis (see the literature cited above). The ExactVariance Analysis is also described below in more detail.

The division into a CD variability component, an MD variabilitycomponent and a residual variability component must be sufficientlyexact in order to be able to draw the correct conclusions for thecontrol in question. An underestimated variable in the assessment of arespective paper quality feature is the residual profile, also referredto simply as residual. The residual fraction of the web deviation isdefined in that it cannot be assigned to either the machine direction(MD) or the cross direction (CD). Hence it characterizes random qualityfaults. The residual fraction is perceived by the paper producer as“live” cross profiles. The cross profile is poor and changes constantly;the peaks in the cross profile are not fixed but sometimes becomeinverted.

Often the paper producer concludes that the cross profile is poor, whichis misleading. The fact is this type of fault cannot be corrected evenby an improved cross profile control, for example with more actuatingelements.

The purpose of the variance component analysis is to put therelationships into perspective and to clearly distinguish which fractionof the displayed profile faults has its origin in stable cross profiledeviations (CD), which fraction comes from temporary production faults(MD), and how large the random fraction of the deviations is.

The variability of the paper web arises therefore from thesuperimposition of three causes:

-   -   a temporally stable CD profile deviation    -   an MD profile deviation which affects the entire web width        uniformly    -   a residual deviation (RES) which arises randomly in place and        time

It has been shown that these three causes of quality deviations can beconsidered as statistically independent of each other, which is animportant precondition for the variance component analysis.

The basic idea behind the Exact Variance Analysis, for example, is tofind an algorithm which correctly determines the amplitudes of the threementioned causes from the measured values of the paper web. Thealgorithm for this Exact Variance Analysis is presented below. It meetsthe following requirements:

-   -   The total variance (TOTAL, TOT) is calculated in accordance with        the general rules of statistics from the variance of all        individual profile measured values in the MD and CD direction        (mean square deviation from the mean value).    -   The amplitudes of the MD, CD and RES deviations are correctly        determined.    -   The sum of the variances of MD, CD and RES result in the total        variance (TOT) in the expected value (TOT).

The relations in question for the Exact Variance Analysis are presentedin the following:

Equation letters:

X: matrix with measured data (each gap is a cross profile)

M, N: number of measured data in the MD and CD direction

i, k counter for the summation

Mean MD profile and CD profile:

${MD}_{k} = {\frac{1}{N} \cdot {\sum\limits_{i = 1}^{N}x_{i,k}}}$${CDi} = {\frac{1}{M} \cdot {\sum\limits_{k = 1}^{M}x_{i,k}}}$

Mean value of all data values:

${m(X)} = {\frac{1}{M \cdot N} \cdot {\sum\limits_{k = 1}^{M}{\sum\limits_{i = 1}^{N}x_{i,k}}}}$

Intermediate variables for calculating the CD and MD variance:

${\text{Var}{ResMD}} = {\frac{1}{\left( {N - 1} \right) \cdot M} \cdot {\sum\limits_{k = 1}^{M}{\sum\limits_{i = 1}^{N}\left( {x_{i,k} - {CD}_{i}} \right)^{2}}}}$${\text{Var}{ResCD}} = {\frac{1}{\left( {M - 1} \right) \cdot N} \cdot {\sum\limits_{k = 1}^{M}{\sum\limits_{i = 1}^{N}\left( {x_{i,k} - {MD}_{k}} \right)^{2}}}}$

Variance fractions (result of the variance analysis):

${\text{Var}{TOT}} = {\frac{1}{{N \cdot M} - 1} \cdot {\sum\limits_{k = 1}^{M}{\sum\limits_{i = 1}^{N}\left( {x_{i,k} - {m(x)}} \right)^{2}}}}$$\text{Var}{{RES}\frac{1}{\left( {M - 1} \right) \cdot \left( {N - 1} \right)} \cdot {\sum\limits_{k = 1}^{M}{\sum\limits_{i = 1}^{N}\left( {x_{i,k} - {CD}_{i} - {MD}_{k} + {m(x)}} \right)^{2}}}}$${\text{Var}{MD}} = {{{\frac{N}{\left( {N - 1} \right) \cdot M} \cdot \left\lbrack {\sum\limits_{k = 1}^{M}\left( {{MD}_{k} - {m(x)}} \right)^{2}} \right\rbrack} - {{\frac{1}{N} \cdot \text{Var}}{ResMD}\text{Var}{CD}}} = {{\frac{M}{\left( {M - 1} \right) \cdot N} \cdot \left\lbrack {\sum\limits_{i = 1}^{M}\left( {{CD}_{i} - {m(x)}} \right)^{2}} \right\rbrack} - {{\frac{1}{M} \cdot \text{Var}}{ResCD}}}}$

Knowledge of the variance scatter can be used to specify confidenceintervals of the variance analysis. The smaller the random sample, thegreater the statistical uncertainty of the analysis and the larger theconfidence intervals.

The specification of confidence intervals helps to relativize the valueof the statistical statements.

For a control system it can be expedient to calculate the variancefractions on the basis of a relatively small number of profiles. Inconjunction with the confidence intervals it is thus possible todistinguish random profile fluctuations from controllable fluctuationswith a real cause.

It has been shown that the variability of the paper web can be assessedfar more informatively if all the individual measured data of the paperweb, in a previous time window, are assessed simultaneously. For theexample mentioned at the beginning it is possible to assess the MDvariability of the paper web in the last 100 seconds on the basis of 500individual measured values instead of typically 20 MD values as wascommonly used hitherto. The fast and precise knowledge of thevariability permits a fast change of the corresponding parameters of thecontrols and filterings if the statistical properties of the paper webchange. Faults of the production process due to overly severe controlinterventions are reduced to a minimum. Such faults can arise when,because of random or non-controllable faults, the control respondsseverely nevertheless and thus carries out unnecessary processinterventions.

The profile measured values contained in a respective measured valueset, which is to be subjected to variance component analysis, can bevariously weighted, in respect of their contribution to the variabilityof the quality feature to be determined at least partially according tohow far back they lie in time compared to the actual moment ofdetermining the variability. In this case, profile measured values lyingrelatively far back can be weighted to a lesser degree.

The variance component analysis can be repeated when a selectable numberof new profile measured values was measured by way of the measurementsystem.

The result of a respective variance component analysis can be drawn onin order to change accordingly the behavior of at least one controlsystem provided to control the quality feature. For example, at leastone control parameter and/or at least one filter parameter of at leastone control system provided to control the quality feature can bevariably adjustable according to the result of a respective varianceanalysis.

The variance component analysis can be based not only on the ExactVariance Analysis just described, but also on at least one of thefollowing algorithms: TAPPI TIP 1101, TAPPI T585, DAHLIN Exact VarianceAnalysis.

With the algorithm used as a basis for the variance component analysisit is possible to calculate short-term deviations, which can also beroughly interpreted as residual components.

The CD variability component and the MD variability component can eachbe divided into a controllable fraction and a non-controllable fraction.

A relatively large residual variability component, compared to thecurrent CD and MD variability components or controllable CD and MDfractions, can be used by way of the control and/or analysis unit 14 inparticular such that at least one control system provided to control thequality feature acts relatively more slowly and/or relatively moreweakly on the production process.

In principle it is also possible for a relatively large residualvariability component, compared to the current CD and MD variabilitycomponents or controllable CD and MD fractions, to be taken intoaccount, such that the measured data are filtered relatively moreintensively prior to calculation of the respective set-point variablefor at least one control system provided to control the qualityfeatures.

As already mentioned it is possible for confidence intervals for the CDprofile and/or the MD profile to be calculated by way of the controland/or analysis device 14 from determined variance values and, in thelight of these confidence intervals, a distinction can be drawn betweena respective profile deviation based on an existing cause and adeviation based, at least essentially, only on a residual variabilitycomponent. At least one control system provided to control the qualityfeature is then activated by way of the control and/or analysis device14 to perform an intervention in the production process if it wasestablished, in the light of at least one confidence interval, that thedeviation in question is based on a really existing cause.

It is also conceivable, in particular for at least one control systemprovided to control the quality feature to be actuated by way of controland/or analysis device 14 to intervene relatively more weakly or not atall in the production process, if it was established, in the light of atleast one confidence interval, that the deviation in question is based,at least essentially, only on a residual variability component.

The calculated variability components and/or confidence intervals canalso be taken into account in the assessment of the quality of a processmodel which serves the quality control and is saved in control and/oranalysis device 14 as software. Any deviation of the default values ofthe process model from the recorded profile measured values isdetermined by way of control and/or analysis device 14 and anydetermined deviation on a relatively high variability of the qualityfeatures and/or a relatively low confidence is weighted relatively morelowly and/or is filtered relatively more intensively prior to furtheruse.

The calculated variability components and/or confidence intervals canalso be taken into account, in particular when there is a change of theprocess model, which serves the quality control, and is saved in controland/or analysis device 14 as data and/or software. The calculatedvariability components and/or confidence intervals can also be takeninto account, for example, through adaptation parameters.

It is also conceivable in particular for the process model to becorrected by way of control and analysis device 14 relatively moreslowly to the process measured in particular by way of the profilemeasured values in the event of a relatively higher variability of thequality feature and/or a relatively lower confidence.

While this invention has been described with respect to at least oneembodiment, the present invention can be further modified within thespirit and scope of this disclosure. This application is thereforeintended to cover any variations, uses, or adaptations of the inventionusing its general principles. Further, this application is intended tocover such departures from the present disclosure as come within knownor customary practice in the art to which this invention pertains andwhich fall within the limits of the appended claims.

List of reference numerals 10 measurement system 12 paper machine 14electrical control and/or analysis unit

1. A method for controlling at least one quality feature of a runningfibrous web during its production, the method comprising the steps of:repeatedly measuring a quality feature in the running fibrous web overan entire width of the fibrous web by way of a measurement system;determining a variability of the quality feature through a variancecomponent analysis by way of an electronic unit, said determining stepincluding the step of conducting said variance component analysis on abasis of a plurality of profile measured values of a measured value set,said measured value set including a plurality of consecutive crossdirection (CD) profiles in a machine direction, said measured value sethaving been recorded during a selectable previous time window; andcontrolling the at least one quality feature dependant on said variancecomponent analysis.
 2. The method of claim 1, wherein said conductingstep further comprising the step of dividing said variability of thequality feature by way of said variance component analysis into a CDvariability component in the cross direction, into a machine direction(MD) variability component in the machine direction and into at leastone of residual variability components and confidence intervals for atleast one of said CD profile and a MD profile calculated from determinedvariance values, said controlling step controlling the at least onequality feature being dependent upon at least one of said variabilitycomponents and said confidence intervals.
 3. The method of claim 2,wherein individual measured values of said measured value set arearranged by said electronic unit in a matrix having said CD profilemeasured values in one of rows and columns of said matrix and said MDprofile measured values in one of said rows and said columns.
 4. Themethod of claim 3, wherein said profile measured values in a respectiveone of said measured value set are variously weighted respective totheir contribution to the variability of the quality feature at leastpartially according to how far back they lie in time as compared to amoment when said determining step is carried out.
 5. The method of claim4, wherein said profile measured values that are farther back in timefrom said moment are weighted relatively more lowly than those saidprofile measure values that are less far back in time from said moment.6. The method of claim 4, wherein said conducting step is performed in arecursive manner.
 7. The method of claim 4, wherein said variancecomponent analysis is repeated when a selectable number of new profilemeasured values are measured by way of said measurement system.
 8. Themethod of claim 4, wherein at least one result of said variancecomponent analysis is drawn on to change a behavior of at least onecontrol system to control the quality feature.
 9. The method of claim 8,wherein at least one of a control parameter and a filter parameter ofsaid at least one control system is variably adjusted dependant on saidat least one result of said variance component analysis.
 10. The methodof claim 4, wherein said variance component analysis is based on atleast one algorithm, said at least one algorithm including one of aTechnical Association of the Pulp and Paper Industry (TAPPI) TechnicalInformation Paper (TIP) 1101, TAPPI T585 and DAHLIN exact varianceanalysis.
 11. The method of claim 10, further comprising the step ofcalculating short-term deviations with said at least one algorithm usedas a basis for said variance component analysis.
 12. The method of claim4, wherein said CD variability component and said MD variabilitycomponent are each divided into a controllable fraction and anon-controllable fraction.
 13. The method of claim 8, further comprisingthe step of determining if said residual variability component isrelatively large compared to one of a current CD variability component,a current MD variability component, a controllable CD fraction and a MDcontrollable fraction and if said residual variability component isrelatively large then using said control system to control the qualityfeature at least one of slower and more weakly during the productionthan if said residual variability component was not relatively large.14. The method of claim 8, further comprising the step of determining ifsaid residual variability component is relatively large compared to oneof a current CD variability component, a current MD variabilitycomponent, a controllable CD fraction and a MD controllable fraction andif said residual variability component is relatively large thenintensively filtering said residual variability component prior to acalculation of a respective set-point variable used by said controlsystem to control the quality feature.
 15. The method of claim 2,wherein said confidence intervals for one of said CD profile and said MDprofile are calculated from determined variance values, using saidconfidence intervals a distinction is drawn between a profile deviationbased on an existing cause and a deviation based on substantially onlysaid residual variability component.
 16. The method of claim 15, furthercomprising the step of intervening in the production of the fibrous webby a control system if in the light of at least one of said confidenceintervals a deviation is based on an existing cause.
 17. The method ofclaim 16, further comprising the step of intervening in the productionof the fibrous web by said control system one of relatively weakly andnot at all if in the light of at least one of said confidence intervalsa deviation is based substantially only on said residual variabilitycomponent.
 18. The method of claim 2, wherein at least one of saidvariability components and said confidence intervals are drawn on forassessing a quality of a process model saved in said electronic unit.19. The method of claim 18, wherein any deviations of default values ofsaid process model from recorded profile measured values is determinedby way of said electronic unit, any of said deviations that aredetermined to be at least one of a relatively high variability of thequality feature and a relatively low confidence is at least one ofweighted relatively more lowly and is filtered relatively moreintensively prior to further use by said electronic unit than if saiddeviations are determined to not be one of a relatively high variabilityof the quality feature and a relatively low confidence.
 20. The methodof claim 18, wherein at least one of said calculated variabilitycomponents and said confidence intervals are used as input to changesaid process model.
 21. The method of claim 20, wherein at least one ofsaid calculated variability components and said confidence intervals areused as input to change said process model by way of adaptationparameters.
 22. The method of claim 20, wherein said process model iscorrected by way of the electronic unit more slowly if there is at leastone of a relatively higher variability of the quality feature and arelatively lower confidence of said profile measured values.
 23. Themethod of claim 2, wherein at least one of a MD variance and a CDvariance is not explicitly determined in said variance componentanalysis.
 24. The method of claim 1, wherein the quality feature is atleast one of moisture, thickness, gsm substance and filler materialcontent of the fibrous web.
 25. An apparatus for controlling at leastone quality feature of a running fibrous web during its production, theapparatus comprising: a measurement system for repeated measurement ofthe at least one quality feature over an entire width of the fibrousweb; and one of an electronic control and an evaluation unit fordetermining a variability of the at least one quality feature by way ofa variance component analysis dependent on a plurality of profilemeasured values of a measured value set, said measured value setincluding several consecutive cross direction (CD) profiles in a machinedirection (MD), said measured value set having been recorded during aselectable previous time window, one of said electronic control and saidevaluation unit controlling the quality feature intervenes in theproduction of the fibrous web at an intensity determined by saidvariance component analysis.
 26. The apparatus of claim 25, wherein oneof said electronic control and said evaluation unit controls thevariability of the quality feature by way of said variance componentanalysis that includes a CD variability component in said CD, and a MDvariability component in said MD and at least one of a residualvariability component and confidence intervals for at least one of saidCD profile and a MD profile calculated from determined variance valuessuch that the control of the quality feature is dependent upon at leastone of said variability components and said confidence intervals. 27.The apparatus of claim 26, wherein at least one of a MD variance and aCD variance is not explicitly determined in said variance componentanalysis.
 28. The method of claim 25, wherein the quality feature is atleast one of moisture, thickness, gsm substance and filler materialcontent of the fibrous web.